This invention, referred to as xe2x80x9cRaster Based Manual Adaptive Target Motion Analysis Evaluationxe2x80x9d (Raster MATE) relates to a method or process for target motion analysis using raw acoustic raster data. More specifically, the invention relates to determination of the solution (relative track position and track motion) of a radiating source, and possibly to determination of solution quality, from acoustic raster.
Determination of certain position and motion parameters, such as location, range, direction and speed, of a target or radiating source, from information items received from the target or radiating source, is a general problem of considerable importance to many types of surveillance systems. For example, a determined location, direction and speed can be used to track a target and anticipate its future location.
Specific terms associated with various systems referred to in this patent application are defined so as to insure common understanding of the terms.
xe2x80x9cAscanxe2x80x9d refers to a single line of acoustic raster energy received at a sensor represented in an amplitude format. The scanning from one reference observation to the next is sometimes referred to as amplitude scanning or Ascan. Displaying the received energy in an amplitude level peak connected line for each observation results in a line of amplitude observations or Ascan at that moment in time.
xe2x80x9cBscanxe2x80x9d refers to a single line of acoustic raster energy received at a sensor represented in an intensity format. The scanning from one reference observation to the next is sometimes referred to as bit scanning or Bscan. Displaying the received energy in a color intensity level for each observation results in a line of observations or Bscan at that moment in time. The use of Bscan raster is referred to in many of the descriptions herein.
xe2x80x9cTarget Positionxe2x80x9d is the location of the target at any point in time. This location could be defined as, and is not limited to xe2x80x9ca Bearing/Range from a sensor location, a Latitude/Longitude location, or X/Y coordinate on a Cartesian Plane.xe2x80x9d
xe2x80x9cTarget Trackxe2x80x9d is a set of target information attributes over time. This set of data points collected over time could be, and is not limited to, bearing information. A data set of bearing could be used to generate a bearing track, which in turn could provide a basis for bearing trend analysis capabilities.
xe2x80x9cTarget Motionxe2x80x9d refers to a direction of motion. This motion normally derived from observing multiple target positions over a specified time period. Track motion attributes could be defined as Course/Speed, Heading/Speed, or X/Y velocities, but are not limited to these attributes.
xe2x80x9cTrack Measurementxe2x80x9d (or Observation) refers to a unique recording of energy radiated from a source or reflected from a target at as specific moment in time. Measurements could be (and are not limited to) Bearing, Conical Angle (in the case of a non-stabilized line array), Range, Inverse Range, Depression/Elevation (D/E), Wave Front Curvature Time Delay, andor Tonal Frequency. Typically track measurements are produced by, or are the output of, a sensor tracker-based function that attempts to follow a specified detected energy source in the sensor""s environment. For the purpose of this disclosure, one of these measurements, namely bearing information, is referred to in many of the descriptions herein. With respect to the invention the method could be applied to any form of data received by the sensor subsystem.
xe2x80x9cSolutionxe2x80x9d (or State Vector) refers to the joining of the target position information with the target track information to define a target""s unique position and motion at a specific moment in time. Using a solution, one could project the target to a future time and resolve or determine the target""s projected position, or project backward in time to resolve where the target was at a moment in time in the past.
xe2x80x9cSolution Generated Track (SGT)xe2x80x9d refers to a set of solution based data points generated by the use of solution extrapolation or projection algorithms. This set of data points collected over time could be, and is not limited to, bearing information. This data set represents a trend of possible target information over time.
xe2x80x9cReceived Energy Trace (RET)xe2x80x9d refers to a set of received high-energy data points. This set of data points collected over time could include, and is not limited to, bearing energy information. An acoustic raster display may display this energy as a function of bearing. A collective set of high-energy data over time creates an energy trace on an acoustic raster display.
Manual, automatic and computer-aided manual methods for determining location, direction and speed of targets (Target Motion Analysis or TMA) are known in the art. These methods of TMA maintain a few basic aspects in common. For the purpose of this paper the Manual Adaptive Target Motion Analysis Evaluation (MATE), Maximum Likelihood Estimator (MLE), Kalman Statistical Track (KAST) and Solution Imaging Target Motion Analysis Evaluator (SITE) algorithms will be addressed. Each of these TMA algorithms requires a set of track measured data or observations. Each adjusts a set of parameters, as for example x/y position and x/y velocity of the target in the Cartesian plane, to make the track parameters agree with the measurements via a functional relationship. In general, for each of these methods, the set of parameters that agrees well with the measured data is deemed to be the estimated resulting solution. In these traditional methods, a Target Motion Analysis algorithm processes xe2x80x9cTrack Measurementxe2x80x9d data to achieve a desired solution.
An example of a manual method is the Manual Adaptive Target Motion Analysis Evaluation (MATE). In this method, the operator defines a set of tracker-based measurements to be used, edits the measurements to remove bad data, and modifies the parameters of the solution in an attempt to minimize the errors between the measured value and the theoretical value at the same moment in time. An example of an automatic or computer-aided method is a Maximum Likelihood Estimator (MLE). In this method, the MLE algorithm automatically defines the tracker measurement data set based upon algorithm control parameters, attempts to pre-edit the measurements to remove bad data, and automatically adjusts parameters in an algorithmic manner so as to achieve the best solution that agrees with the measurement data set. A second example of an automatic or computer-aided method is a Kalman Statistical Track (KAST). In this method, the KAST starts with a guess at the solution, receives tracker-based measurement data points one at a time and pre-edits the measurements with respect to bad data parameters, then uses the measurements to improve the guess in attempts to narrow in on the best solution with respect to the measurement data set. An example of a manually controlled computer-aided method is a Solution Imaging Target Motion Analysis Evaluator (SITE). In this method, the operator defines a set of tracker-based measurements to be used, edits the measurements to remove bad data, and selects parameters for solution image generation. The image generation process: generates a matrix of the solutions. Each solution is used to quantify the errors between a measured value and a theoretical value at the same moment in time. The resulting matrix provides a graphical representation of solution possibilities coded with respect to the solution error detected. MATE, MLE, KAST and SITE work on a predefined set of tracker-based measurement data per algorithm execution. All algorithms require some type of data editing function to remove bad tracker data from the data set prior to algorithm execution. MLE and KAST adjust the parameters automatically, while MATE and SITE require an operator to adjust the parameters.
An underlying assumption for each of these algorithms is that high-quality tracker-based measurement data is provided to the TMA algorithms from a measurement subsystem, and that the received measurement data has already been (or is already) associated with the target of interest. A problem with current acoustic systems is the inability of the tracker function to track the target of interest and to generate high-quality track measurement data sufficient to allow a solution to be resolved by the TMA algorithms. Erratic or poor tracker data may occur under any of the following conditions, among others:
I. Close-in high bearing rate target situations;
II. Low signal-to-noise (SNR) target environments; and
III. High-density multi-target environments.
Historically, the acoustic portion of the problem has been to identify a target and support automatic or manual tracking of the target for TMA algorithm analysis. The output of the tracking function is ideally well-defined track measurements that are provided as inputs to the TMA algorithm functions. The abovementioned target situations may result in the inability to maintain automatic track on the target, so that the data provided to the TMA algorithms is insufficient for extraction of a solution. Transferring to manual track mode often fails to improve system performance. Some of the reasons for these tracker-based problems include, but are not limited to:
I. trackers are nominally tuned to support a specific range of target bearing rate;
II. during highly variable and high bearing rate scenarios, automatic trackers fail to maintain track on the energy source;
III. automatic trackers are also not capable of tracking the low energy levels experienced for low SNR targets; and
IV. during high-density scenarios, multiple energy sources exist, which tend to confuse the automatic trackers when the energy traces cross, or a stronger trace masks a weaker trace. When energy traces cross, the automatic tracker oftentimes follows the incorrect energy trace.
Any or all of the preceding reasons could result in incorrect data being generated by the tracking function, with consequent poor TMA algorithm results.
Attempts to work around the above situations usually involve having the acoustic operator put the tracker-based function into the manual state, and manually attempting to define the bearing information based upon a trace observed on a raster display (a plot of time or range versus bearing angle, generally with intensity as a parameter). This process of manually marking bearing information depends upon placement of a cursor at a particular position on a xe2x80x9cwater-fallingxe2x80x9d acoustic Bscan raster. Such placement tends to be inaccurate or differs from one scan to the next. Unfortunately, manually generated data usually fails to resolve the problem because it provides noisy data to TMA algorithms. Limitations associated with the generation of manual data is partly due to:
I. inadequate bearing (cursor) resolution on sonar displays. Since bearing (cursor) resolution is a function of sensor beam width, sensor dependent inaccuracies are introduced into the data, when it is sent to the TMA algorithms The limitation of the displays tends to be a function of the sensor beam width. Most displays provide information for operator observation at this level. For automatic trackers, the tracking function uses multiple beams to interpolate a more refined beam set for attempting to follow the target. In manual track function, no additional interpolated beam set normally exists, thereby limiting the operator""s ability to generate high-quality data.
II. attempting to mark data on a display that is water-falling under the operator""s control of the manual tracker cursor; and
III. data generated manually is a single point pick method based upon the cursor position on the raster, which adds noise to the data when sent to the TMA algorithms.
Because of the above-mentioned problems with automatic and manual data generation, data transferred to TMA algorithms oftentimes is not adequate to support resolution of a target solution. In the above environments, lost automatic tracker data, and noisy manually generated acoustic data contains much misleading information, complicating the TMA process, which in turn may result in unsatisfactory overall TMA performance.
In all the abovedescribed situations, the TMA algorithm processes are provided with data to be used in resolving the target solution. If the data provided is misleading, inaccurate, or an incomplete set of measurements, then the quality and possibility of timely resolution of a solution for the target diminishes. In each abovedescribed situation, the tracker data fails to provide the information observed by the operator on the acoustic Bscan raster data.
Hence, there is a need for a system that provides fast and accurate target solution along with a merit of solution quality for evaluation. The method disclosed below negates the requirement of tracker-based measurement data by using the raw acoustic Bscan raster data for algorithmic solution generation.
A method for Target Motion Analysis according to an aspect of the invention, or for determining location information associated with a contact based upon acoustic raster data received from at least one sensor, comprises the step of deriving target trace data from coordinates associated with the acoustic raster data. The derived target trace data is compared with the raster data, and the derived target trace data is updated or adjusted by resetting at least one of time, bearing and range parameters associated with the derived target trace. This resetting of the time, bearing andor range parameters is for minimizing the differences between the target trace data and the raster data.
A system according to another aspect of the invention is for determining location information associated with a contact. The system comprises a detector for detecting and processing acoustic data to generate raster data associated with a contact. The system also includes a computer having a memory and a processor responsive to the generated raster data for deriving target trace data from coordinates associated with the generated raster data. The computer includes a user interface enabling visual comparison of the target trace data with the raster data, and means for adjusting at least one of time, bearing and range parameters associated with the derived target trace, to cause the processor to update the target trace data for minimizing the differences between the target trace data and the raster data.
A method according to another aspect of the invention is for deriving a contact solution based upon raster data. The method includes the step of processing received acoustic data to generate raster data associated with a contact. The method also includes the steps of deriving target trace data from coordinates associated with the acoustic raster data, and comparing the derived target trace data with the raster-data. The derived target trace data is updated by adjusting at least one of time, bearing and range parameters associated with the derived target trace for minimizing the differences between the target trace data and the raster data.
A system for deriving a solution on a contact includes a detector for detecting and processing acoustic data, to generate raster data associated with the contact. The system also includes a computer having a memory and a processor responsive to the generated raster data for deriving target trace data from coordinates associated with the generated raster data. The computer includes a user interface, which enables visual comparison of the target trace data with the raster data, and also includes an arrangement or means for adjusting at least one of time, bearing and range parameters associated with the derived target trace, to cause the processor to update the target trace data for minimizing the differences between the target trace data and the raster data. The processor provides range, bearing, course and speed of the contact based on the acoustic data.